
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "35"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459998	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.217391	-0.468721	0.647661	-0.962435	-0.425682	-0.540548	0.397651	0.541870	0.5754	0.5709	0.3699	nan	nan
2459997	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.990783	-0.745013	1.365534	-1.079050	0.624719	-1.281709	-0.855367	-0.290321	0.5992	0.5985	0.3756	nan	nan
2459996	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	1.191450	-0.923458	1.330918	-1.161288	0.325737	-1.241754	-1.214759	0.047600	0.5987	0.6010	0.3897	nan	nan
2459995	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.957257	-1.035387	1.271794	-1.278568	0.487754	-0.750362	1.019772	-0.240427	0.5976	0.5980	0.3745	nan	nan
2459994	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.740448	-0.737597	1.172862	-1.279351	1.500006	-1.187602	-0.942289	-0.334834	0.5894	0.5861	0.3692	nan	nan
2459993	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	1.044243	-0.695106	1.543623	-1.076271	0.223636	-1.197623	-1.175203	-0.233098	0.5848	0.6004	0.3800	nan	nan
2459991	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.763420	-0.479670	1.243522	-1.131971	3.506808	-1.025913	-0.648412	-0.005058	0.5860	0.5750	0.3757	nan	nan
2459990	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.036463	-0.091180	0.898356	-1.069083	3.377427	-0.841562	5.573222	0.369446	0.5890	0.5813	0.3737	nan	nan
2459989	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	-0.428415	-0.152419	0.796288	-0.999598	-0.106979	-1.114147	5.145883	-0.256880	0.5853	0.5804	0.3748	nan	nan
2459988	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	-0.832995	-0.352243	0.773040	-1.085336	-0.151161	-1.178876	11.578987	0.477541	0.5678	0.5638	0.3579	nan	nan
2459987	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.862421	-0.986712	1.343432	-1.233152	1.059742	-0.723704	0.433797	1.000617	0.5954	0.5908	0.3644	nan	nan
2459986	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.236096	-1.038955	0.960424	-1.400573	0.925460	-0.798802	0.766003	2.946872	0.6063	0.6089	0.3307	nan	nan
2459985	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.233803	-0.895975	0.779153	-1.223465	-0.191197	-0.951827	-0.085600	-0.203459	0.5901	0.5835	0.3721	nan	nan
2459984	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.964689	-1.037666	1.358449	-1.124299	1.401420	-0.719394	1.133341	1.655765	0.5924	0.5946	0.3458	nan	nan
2459983	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.113954	-0.651690	0.798673	-1.325529	1.596388	-0.865831	-0.428253	1.925876	0.6217	0.6328	0.2974	nan	nan
2459982	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.314018	0.241367	0.578190	-1.246075	-1.076124	-0.702462	-0.563757	0.781147	0.6458	0.6441	0.2999	nan	nan
2459981	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.468343	-0.105900	1.613025	-1.059013	0.691945	-1.307076	-0.867541	-0.471473	0.5902	0.5822	0.3702	nan	nan
2459980	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.247382	-0.591447	1.188184	-1.371521	0.303753	-1.508892	-0.177842	-0.239066	0.6295	0.6286	0.3122	nan	nan
2459979	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.399447	-0.114550	1.226782	-1.355234	0.489041	-1.203919	-0.615278	0.220646	0.5833	0.5782	0.3686	nan	nan
2459978	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.540345	-0.004953	1.436131	-1.250089	0.672972	-1.323856	-1.147360	-0.115028	0.5793	0.5718	0.3768	nan	nan
2459977	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.816730	-0.468489	1.174818	-1.327826	0.480446	-1.328108	-1.766100	-0.466710	0.5505	0.5460	0.3338	nan	nan
2459976	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.053891	-0.123478	0.827086	-1.444474	-0.075048	-0.641323	-0.696254	0.418209	0.5977	0.5886	0.3734	nan	nan

Antenna Season Report Notebook¶

Antenna 35 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 35, Node N06:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 35: 2459998

Antenna 35: 2459997

Antenna 35: 2459996

Antenna 35: 2459995

Antenna 35: 2459994

Antenna 35: 2459993

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Antenna 35: 2459986

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Antenna 35: 2459980

Antenna 35: 2459979

Antenna 35: 2459978

Antenna 35: 2459977

Antenna 35: 2459976

Antenna Season Report Notebook¶

Antenna 35 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 35, Node N06:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 35: 2459998

Antenna 35: 2459997

Antenna 35: 2459996

Antenna 35: 2459995

Antenna 35: 2459994

Antenna 35: 2459993

Antenna 35: 2459991

Antenna 35: 2459990

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Antenna 35: 2459988

Antenna 35: 2459987

Antenna 35: 2459986

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Antenna 35: 2459981

Antenna 35: 2459980

Antenna 35: 2459979

Antenna 35: 2459978

Antenna 35: 2459977

Antenna 35: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶